The difference between big data and dark data

There is a lot of buzz around the terms “big data” and “dark data.” But what do they actually mean?

In short, big data is the term used to describe the large volume of data that is being collected and processed by businesses and organizations. Your business likely uses big data in some way, whether through customer data, website analytics, or social media data.

Dark data, on the other hand, is a term used to describe the large volume of data that is collected but never actually used. This could be because it’s unstructured or unorganized, making it difficult to process. Or, it could be because it’s simply not relevant to the organization’s goals.

Despite their differences, big data and dark data both present business opportunities and challenges. When it comes to big data, the challenge is often collecting and storing such a large volume of data.

In this article, we will look at the primary differences between big data and dark data. We will also explore the opportunities and challenges that each presents for businesses.

Defining data

As a business, you use data in a myriad of ways every single day. This data can be used to better understand your customers, make decisions about product development, and track your business goals.

Data comes in many forms, but it can generally be divided into two categories: structured data and unstructured data.

Structured data is data that is organized in a specific way. It is typically stored in databases and can be easily accessed, sorted, and analyzed.

Unstructured data is data that is not organized in a specific way. It includes things like emails, social media posts, images, and videos. This type of data is more difficult to analyze because it isn’t as easy to access and sort.

Most businesses use a mix of both structured and unstructured data. However, the type of data that you collect will depend on your business goals.

For example, if you’re a retail business, you might collect structured data like customer purchase history and unstructured data like customer reviews.

What is big data?

Big data is simply a term used to describe businesses’ large volumes of data. This data can come from various sources, including social media, website analytics, customer data, and more.

The challenge with big data is that it can be difficult to manage and process such a large volume of information. This is where big data analytics comes in. Big data analytics is the process of extracting valuable insights from big data.

Through big data analytics, businesses can better understand their customers, make decisions about product development, and track their business goals.

What is dark data?

Now that we’ve defined big data, let’s take a look at dark data.

Dark data is a term used to describe the large volume of data that is collected but never actually used. This could be because it’s unstructured or unorganized, making it difficult to process.

Despite the fact that it’s never been used, dark data still takes up valuable storage space and resources. In fact, some estimates suggest that as much as 90% of corporate data is dark data.

So why do businesses continue to collect dark data?

1. They’re not sure what data is valuable and what isn’t.

Just because data is collected doesn’t mean that it’s valuable.

Businesses need to be strategic about the data they collect and focus on collecting data that will be useful for their specific goals. Otherwise, they run the risk of collecting a lot of unnecessary dark data.

2. They don’t have the resources to process it.

Even if businesses are able to collect valuable data, they might not have the resources to process it. This is often the case with big data.

Big data can be difficult to manage and process because of its size and complexity. As a result, businesses might not have the resources or expertise to extract valuable insights from them.

3. They’re not sure how to use it.

Another reason businesses might collect dark data is that they’re not sure how to use it. This is often the case with unstructured data, like social media posts or customer reviews.

Unstructured data can be challenging to analyze because it isn’t organized in a specific way. As a result, businesses might not know how to extract valuable insights from it.

4. They don’t have the time to use it.

Finally, businesses might collect dark data because they simply don’t have the time to use it. This is often the case with small businesses that don’t have the resources to process and analyze large volumes of data.

How can businesses use dark data?

Even though dark data can be challenging to process and analyze, there are still ways for businesses to use it.

1. Social media monitoring

One way businesses can use dark data is by using it to monitor social media. This can be done by using a tool that collects and analyzes social media posts.

This can be useful for businesses that want to understand what people are saying about their brand or industry. It can also be used to track competitors’ activity and identify new trends.

2. Text analysis

Another way businesses can use dark data is by using it to perform text analysis. Text analysis operates by extracting insights from text data.

If you want to better understand customer sentiment, text analysis can be a helpful tool. It can also be used to identify new trends and understand what people are saying about your brand or industry.

3. Predictive analytics

Predictive analytics is a type of data analysis that uses historical data to predict future events.

Predictive analytics can be used with dark data to predict things like customer behavior, website traffic, and sales.

4. Data mining

Data mining is the process of extracting insights from data. It can be used to find patterns and relationships in data sets.

Data mining can be used with dark data to understand customer behavior and identify new trends. Plus, data mining can be used to generate hypotheses that can be tested with further analysis.

5. Data visualization

Data visualization is the process of creating visual representations of data. It can be used to understand complex data sets and to find patterns and relationships.

Data visualization can be used with dark data to understand customer behavior and identify new trends. Data visualization can also be used to generate hypotheses that can be tested with further analysis.

The bottom line

Despite its challenges, dark data can be used in a number of ways to help businesses grow. Dark data can be used in conjunction with big data to help companies gain the most value from their data.